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Register a new userEmploying low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement
توظيف ميزات الكلام الزمنية المرحلة المنخفضة لتدريب قناع نسبة مثالية في تحسين الكلام
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تتابع طريقة تحسين الكلام القائم على اخفاء قناع مضاعف ينطبق على الطيفية من الكلام الفاسد من ضوضاء الإدخال، وغالبا ما تستخدم شبكة عميقة العصبية (DNN) لتعلم القناع. على وجه الخصوص، يمكن أن تكون الميزات الشائعة الاستخدام للتعرف على الكلام التلقائي بمثابة مدخلات DNN لتعلم القناع حسن التصرف الذي يقلل بشكل كبير من تشويه الضوضاء للكلمات المعالجة. تقترح هذه الدراسة إعادة معالجة ميزات خطاب المدخلات لمقنعة النسبة المثالية (IRM) - DNN بواسطة Lowpass Filtering من أجل تخفيف مكونات الضوضاء. على وجه الخصوص، فإننا نوظف تحويل المويجات المنفصلة (DWT) لتحلل تسلسل ميزة الكلام الزمنية وتوسيع نطاق معاملات التفاصيل، مما يتوافق مع الجزء المرتفع من التسلسل. تكشف التجارب الأولية التي أجراها مجموعة فرعية من تيميت كوربوس أن الطريقة المقترحة يمكن أن تجعل IRM الناتجة تحقيق جودة أعلى للكلام وضوحا للإشارات الاضافة عن الضوضاء الخليفة مقارنة مع IRM الأصلي، مما يشير إلى أن تسلسل الميزات الزمنية المرشح ل Lowpass يمكن أن يتعلم متفوقة شبكة IRM لتعزيز الكلام.
The masking-based speech enhancement method pursues a multiplicative mask that applies to the spectrogram of input noise-corrupted utterance, and a deep neural network (DNN) is often used to learn the mask. In particular, the features commonly used for automatic speech recognition can serve as the input of the DNN to learn the well-behaved mask that significantly reduce the noise distortion of processed utterances. This study proposes to preprocess the input speech features for the ideal ratio mask (IRM)-based DNN by lowpass filtering in order to alleviate the noise components. In particular, we employ the discrete wavelet transform (DWT) to decompose the temporal speech feature sequence and scale down the detail coefficients, which correspond to the high-pass portion of the sequence. Preliminary experiments conducted on a subset of TIMIT corpus reveal that the proposed method can make the resulting IRM achieve higher speech quality and intelligibility for the babble noise-corrupted signals compared with the original IRM, indicating that the lowpass filtered temporal feature sequence can learn a superior IRM network for speech enhancement.
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https://aclanthology.org/
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in various fields of life, whether medical or security or industrial techniques. Accordingly,
many related systems were developed, which differ from each otherin fea
ture extraction
methods and classification methods.
In this research,three systems have been created for speech recognition.They differ
from each other in the used methods during the stage of features extraction.While the first
system used MFCC algorithm, the second system used LPCC algorithm, and the third
system used PLP algorithm.All these three systems used HMM as classifier.
At the first, the performance of the speechrecognitionprocesswas studied and
evaluatedfor all the proposedsystems separately. After that, the combination algorithm was
applied separately on eachpair of the studied system algorithmsin order to study the effect
of using the combination algorithm onthe improvement of the speech recognition process.
Twokinds of errors(simultaneous errors and dependent errors) were usedto evaluate
the complementaryof each pair of the studied systems, and to study the effectiveness of the
combination on improving the performance of speech recognition process. It can be seen
from the results of the comparison that the best improvement ratio of speech recognition
has been obtained in the case of collection MFCC and PLP algorithms with recognition
ratio of 93.4%.
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